examples/bloom_embeddings.ipynb

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   "source": [
    "%pip install mmh3 numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Bloom embeddings algorithm\n",
    "\n",
    "In a normal embedding table, each word-string is mapped to a distinct ID.\n",
    "Usually these IDs will be sequential, so if you have a vocabulary of 100 words,\n",
    "your words will be mapped to numbers `range(100)`. The sequential IDs can then\n",
    "be used as indices into an embedding table: if you have 100 words in your\n",
    "vocabulary, you have 100 rows in the table, and each word receives its own\n",
    "vector.\n",
    "\n",
    "However, there's no limit to the number of unique words that might occur in a\n",
    "sample of text, while we definitely want a limited number of rows in our\n",
    "embedding table. Some of the rows in our table will therefore need to be shared\n",
    "between multiple words in our vocabulary. One obvious solution is to set aside a\n",
    "single vector in the table. Words 0-98 will each receive their own vector, while\n",
    "all other words are assigned to vector 99.\n",
    "\n",
    "However, this asks vector 99 to do a lot of work. What if we gave more vectors\n",
    "to the unknown words?"
   ]
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   "source": [
    "def get_row(word_id, number_vector=100, number_oov=10):\n",
    "    if word_id < (number_vector - number_oov):\n",
    "        return word_id\n",
    "    else:\n",
    "        return number_vector + (word_id % number_oov)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives the model a little more resolution for the unknown words. If all\n",
    "out-of-vocabulary words are assigned the same vector, then they'll all look\n",
    "identical to the model. Even if the training data actually includes information\n",
    "that shows two different out-of-vocabulary words have important, different\n",
    "implications -- for instance, if one word is a strong indicator of positive\n",
    "sentiment, while the other is a strong indicator of negative sentiment -- the\n",
    "model won't be able to tell them apart. However, if we have 10 buckets for the\n",
    "unknown words, we might get lucky, and assign these words to different buckets.\n",
    "If so, the model would be able to learn that one of the unknown-word vectors\n",
    "makes positive sentiment more likely, while the other vector makes negative\n",
    "sentiment more likely.\n",
    "\n",
    "If this is good, then why not do more of it? Bloom embeddings are like an\n",
    "extreme version, where _every_ word is handled like the unknown words above:\n",
    "there are 100 vectors for the \"unknown\" portion, and 0 for the \"known\" portion.\n",
    "\n",
    "So far, this approach seems weird, but not necessarily good. The part that makes\n",
    "it unfairly effective is the next step: by simply doing the same thing multiple\n",
    "times, we can greatly improve the resolution, and have unique representations\n",
    "for far more words than we have vectors. The code in full:"
   ]
  },
  {
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   "source": [
    "import numpy\n",
    "import mmh3\n",
    "\n",
    "def allocate(n_vectors, n_dimensions):\n",
    "    table = numpy.zeros((n_vectors, n_dimensions), dtype='f')\n",
    "    table += numpy.random.uniform(-0.1, 0.1, table.size).reshape(table.shape)\n",
    "    return table\n",
    "\n",
    "def get_vector(table, word):\n",
    "    hash1 = mmh3.hash(word, seed=0)\n",
    "    hash2 = mmh3.hash(word, seed=1)\n",
    "    row1 = hash1 % table.shape[0]\n",
    "    row2 = hash2 % table.shape[0]\n",
    "    return table[row1] + table[row2]\n",
    "\n",
    "def update_vector(table, word, d_vector):\n",
    "    hash1 = mmh3.hash(word, seed=0)\n",
    "    hash2 = mmh3.hash(word, seed=1)\n",
    "    row1 = hash1 % table.shape[0]\n",
    "    row2 = hash2 % table.shape[0]\n",
    "    table[row1] -= 0.001 * d_vector\n",
    "    table[row2] -= 0.001 * d_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we've used two keys, assigned from two random hash functions.\n",
    "It's unlikely that two words will collide on both keys, so by simply summing the\n",
    "vectors together, we'll assign most words a unique representation.\n",
    "\n",
    "For the sake of illustration, let's step through a very small example,\n",
    "explicitly.\n",
    "\n",
    "Let's say we have this vocabulary of 20 words:"
   ]
  },
  {
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   "source": [
    "vocab = ['apple', 'strawberry', 'orange', 'juice', 'drink', 'smoothie',\n",
    "         'eat', 'fruit', 'health', 'wellness', 'steak', 'fries', 'ketchup',\n",
    "         'burger', 'chips', 'lobster', 'caviar', 'service', 'waiter', 'chef']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll embed these into two dimensions. Normally this would give us a table of\n",
    "`(20, 2)` floats, which we would randomly initialise. With the hashing trick, we\n",
    "can make the table smaller. Let's give it 15 vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "elapsed": 8,
     "status": "ok",
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    },
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   "source": [
    "normal_embed = numpy.random.uniform(-0.1, 0.1, (20, 2))\n",
    "hashed_embed = numpy.random.uniform(-0.1, 0.1, (15, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the normal table, we want to map each word in our vocabulary to its own\n",
    "vector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1648197801914,
     "user": {
      "displayName": "Vincent D. Warmerdam",
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   "source": [
    "word2id = {}\n",
    "def get_normal_vector(word, table):\n",
    "    if word not in word2id.keys():\n",
    "        word2id[word] = len(word2id)\n",
    "    return table[word2id[word]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hashed table only has 15 rows, so some words will have to share. We'll\n",
    "handle this by mapping the word into an arbitrary integer – called a \"hash\n",
    "value\". The hash function will return an arbitrary integer, which we'll mod into\n",
    "the range `(0, 15)`. Importantly, we need to be able to compute _multiple,\n",
    "distinct_ hash values for each key – so Python's built-in hash function is\n",
    "inconvenient. We'll therefore use MurmurHash.\n",
    "\n",
    "Let's see what keys we get for our 20 vocabulary items, using MurmurHash:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "timestamp": 1648197804508,
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      "displayName": "Vincent D. Warmerdam",
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    },
    "id": "Gs69d2KRxmg9"
   },
   "outputs": [],
   "source": [
    "hashes1 = [mmh3.hash(w, 1) % 15 for w in vocab]\n",
    "assert hashes1 == [3, 6, 4, 13, 8, 3, 13, 1, 9, 12, 11, 4, 2, 13, 5, 10, 0, 2, 10, 13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, some keys are shared between multiple words, while 2/15 keys are\n",
    "unoccupied. This is obviously unideal! If multiple words have the same key,\n",
    "they'll map to the same vector – as far as the model is concerned, \"strawberry\"\n",
    "and \"heart\" will be indistinguishable. It won't be clear which word was used –\n",
    "they have the same representation.\n",
    "\n",
    "To address this, we simply hash the words again, this time using a different\n",
    "seed – so that we get a different set of arbitrary keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1648197804508,
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      "displayName": "Vincent D. Warmerdam",
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     },
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    },
    "id": "acpOxkljynPo"
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "hashes2 = [mmh3.hash(w, 2) % 15 for w in vocab]\n",
    "assert len(Counter(hashes2).most_common()) == 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This one's even worse – 3 keys unoccupied! But our strategy is not to keep drawing until we get a favorable seed. Instead, consider this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1648197805024,
     "user": {
      "displayName": "Vincent D. Warmerdam",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gh4KYzhhhK0YDTnAQsUIaQPw-0dKIP-kLBID7nFdQ=s64",
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     },
     "user_tz": -60
    },
    "id": "W7tfxLQBytWP"
   },
   "outputs": [],
   "source": [
    "assert len(Counter(zip(hashes1, hashes2))) == 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By combining the results from the two hashes, our 20 words distribute perfectly,\n",
    "into 20 unique combinations. This makes sense: we expect to have some words\n",
    "overlapping on one of the keys, but we'd have to be very unlucky for a pair of\n",
    "words to overlap on _both_ keys.\n",
    "\n",
    "This means that if we simply add the two vectors together, each word once more\n",
    "has a unique representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1648197805764,
     "user": {
      "displayName": "Vincent D. Warmerdam",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gh4KYzhhhK0YDTnAQsUIaQPw-0dKIP-kLBID7nFdQ=s64",
      "userId": "05641618555626735638"
     },
     "user_tz": -60
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple -0.033 -0.012\n",
      "strawberry -0.023 -0.037\n",
      "orange 0.158 -0.031\n",
      "juice -0.045 0.139\n",
      "drink 0.024 0.030\n",
      "smoothie 0.121 0.076\n",
      "eat -0.093 0.153\n",
      "fruit 0.083 0.052\n",
      "health 0.064 -0.046\n",
      "wellness 0.143 0.112\n",
      "steak 0.011 -0.097\n",
      "fries 0.036 0.041\n",
      "ketchup 0.081 0.029\n",
      "burger -0.045 0.139\n",
      "chips -0.118 -0.090\n",
      "lobster 0.016 -0.107\n",
      "caviar -0.033 -0.012\n",
      "service 0.081 0.029\n",
      "waiter 0.179 -0.038\n",
      "chef -0.047 0.062\n"
     ]
    }
   ],
   "source": [
    "for word in vocab:\n",
    "    key1 = mmh3.hash(word, 0) % 15\n",
    "    key2 = mmh3.hash(word, 1) % 15\n",
    "    vector = hashed_embed[key1] + hashed_embed[key2]\n",
    "    print(word, '%.3f %.3f' % tuple(vector))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a function that maps our 20 words to 20 unique vectors – but we're\n",
    "storing weights for only 15 vectors in memory. Now the question is: will we be\n",
    "able to find values for these weights that let us actually map words to useful\n",
    "vectors?\n",
    "\n",
    "Let's do a quick experiment to see how this works. We'll assign \"true\" values\n",
    "for our little vocabulary, and see how well we can approximate them with our\n",
    "compressed table. To get the \"true\" values, we _could_ put the \"science\" in data\n",
    "science, and drag the words around into reasonable-looking clusters. But for our\n",
    "purposes, the actual \"true\" values don't matter. We'll therefore just do a\n",
    "simulation: we'll assign random vectors as the \"true\" state, and see if we can\n",
    "learn values for the hash embeddings that match them.\n",
    "\n",
    "The learning procedure will be a simple stochastic gradient descent:"
   ]
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     "background_save": true
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "aborted",
     "timestamp": 1648199186370,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499 43.47128495286\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import numpy.random as random\n",
    "import mmh3\n",
    "\n",
    "random.seed(0)\n",
    "nb_epoch = 500\n",
    "learn_rate = 0.001\n",
    "nr_hash_vector = 1000\n",
    "\n",
    "words = [str(i) for i in range(2000)]\n",
    "true_vectors = numpy.random.uniform(-0.1, 0.1, (len(words), 10))\n",
    "hash_vectors = numpy.random.uniform(-0.1, 0.1, (nr_hash_vector, 10))\n",
    "examples = list(zip(words, true_vectors))\n",
    "\n",
    "for epoch in range(nb_epoch):\n",
    "    random.shuffle(examples)\n",
    "    loss=0.\n",
    "    for word, truth in examples:\n",
    "        key1 = mmh3.hash(word, 0) % nr_hash_vector\n",
    "        key2 = mmh3.hash(word, 1) % nr_hash_vector\n",
    "        hash_vector = hash_vectors[key1] + hash_vectors[key2]\n",
    "        diff = hash_vector - truth\n",
    "        hash_vectors[key1] -= learn_rate * diff\n",
    "        hash_vectors[key2] -= learn_rate * diff\n",
    "        loss += (diff**2).sum()\n",
    "print(epoch, loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's worth taking some time to play with this simulation. You can start by doing\n",
    "some sanity checks:\n",
    "\n",
    "- How does the loss change with `nr_hash_vector`?\n",
    "- If you remove `key2`, does the loss go up?\n",
    "- What happens if you add more hash keys?\n",
    "- What happens as the vocabulary size increases?\n",
    "- What happens when more dimensions are added?\n",
    "- How sensitive are the hash embeddings to the initial conditions? If we change the random seed, do we ever get unlucky?\n",
    "\n",
    "If you play with the simulation for a while, you'll start to get a good feel for\n",
    "the dynamics, and hopefully you'll have a clear idea of why the technique works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TuRoY34yQb0v",
    "tags": []
   },
   "source": [
    "## Bonus Section \n",
    "\n",
    "To make it easier for folks to try out a whole bunch of settings we'd added a little bit of code below that makes it easier to get relevant visuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2919,
     "status": "ok",
     "timestamp": 1648200042349,
     "user": {
      "displayName": "Vincent D. Warmerdam",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14Gh4KYzhhhK0YDTnAQsUIaQPw-0dKIP-kLBID7nFdQ=s64",
      "userId": "05641618555626735638"
     },
     "user_tz": -60
    },
    "id": "NPVKX_pbXJYs",
    "outputId": "fc046666-d690-426d-b8a7-dc557f12832d",
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install altair pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "nHd1wo6m1q-J"
   },
   "outputs": [],
   "source": [
    "from functools import reduce \n",
    "\n",
    "\n",
    "def calc_losses(epochs=500, seed=0, learn_rate=0.001, nr_hash_vector=1000, n_hash=3, n_words=1000, size_vector=10):\n",
    "    random.seed(seed)\n",
    "    nb_epoch = epochs\n",
    "    learn_rate = learn_rate\n",
    "    nr_hash_vector = nr_hash_vector\n",
    "\n",
    "    words = [str(i) for i in range(n_words)]\n",
    "    true_vectors = numpy.random.uniform(-0.1, 0.1, (len(words), size_vector))\n",
    "    hash_vectors = numpy.random.uniform(-0.1, 0.1, (nr_hash_vector, size_vector))\n",
    "    examples = list(zip(words, true_vectors))\n",
    "\n",
    "    losses = []\n",
    "    for epoch in range(nb_epoch):\n",
    "        random.shuffle(examples)\n",
    "        loss=0.\n",
    "        for word, truth in examples:\n",
    "            keys = [mmh3.hash(word, k) % nr_hash_vector for k in range(n_hash)]\n",
    "            hash_vector = reduce(lambda a, b: a + b, [hash_vectors[k] for k in keys])\n",
    "            diff = hash_vector - truth\n",
    "            for key in keys:\n",
    "                hash_vectors[key] -= learn_rate * diff\n",
    "            loss += (diff**2).sum()\n",
    "        losses.append(loss)\n",
    "    return losses\n",
    "\n",
    "data = []\n",
    "for n_hash in [1, 2, 3, 4, 5]:\n",
    "    losses = calc_losses(nr_hash_vector=2_000, n_words=10_000, n_hash=n_hash, epochs=150)\n",
    "    data = data + [{\"loss\": l, \"nr_hash_vector\": nr_hash_vector, \"n_hash\": str(n_hash), \"epoch\": e} for e, l in enumerate(losses)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "P0Q0k9bjXMm3"
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       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "\n",
    "source = pd.DataFrame(data)\n",
    "\n",
    "(alt.Chart(source)\n",
    "  .mark_line()\n",
    "  .encode(x='epoch', y='loss', color='n_hash')\n",
    "  .properties(width=600, height=250)\n",
    "  .interactive())"
   ]
  }
 ],
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   "version": ""
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